Methods for Ecological Analysis

PLAN64152 Methods for Ecological Analysis 15 Semester 2 Postgraduate Taught Level 6 1 FHEQ level (Framework for Higher Education Qualifications) ' Masters/Integrated Masters P4 ' European Credit Transfer & Accumulation System Rating : 7.5 vidence comes in many forms, both quantitative e.g., numbers, and qualitative e.g., expert opinion, and all have value provided that they are robustly and objectively collected and analysed. Such evidence is critical to demonstrate the effectiveness or not of interventions, or to inform decision-making and policy development. Consequently, to realise the transformative change needed in conservation, and when implementing interventions that are in themselves contested or in locations where they may be considered controversial, the evidence base must be convincing.Students on NR3 will have encountered a range of methodological approaches during Semester 1. The present module will begin by reviewing and recapping those previously covered. Thereafter, the course unit will consolidate, and compliment knowledge previously learned to set into context the toolkit of approaches available to data generation in social-ecological studies. Thus, qualitative and quantitative analyses will be introduced and reviewed for their role in evidence generation, with an emphasis on mixed-method approaches.Students taking the course unit will broadly follow the scientific process: observing and questioning, identifying research areas, generating hypotheses, experimental set ups, data analysis, reporting results. A central theory will be the reduction of uncertainty and thus, the sources of uncertainty in data collection and analysis e.g., reduction of error and redundancy in data collection and handling. Throughout the unit, students will be encouraged to consider their dissertation topics during this process by identifying which methods they may wish to employ / are feasible given the topic of interest.    vidence comes in many forms, both quantitative e.g., numbers, and qualitative e.g., expert opinion, and all have value provided that they are robustly and objectively collected and analysed. Such evidence is critical to demonstrate the effectiveness or not of interventions, or to inform decision-making and policy development. Consequently, to realise the transformative change needed in conservation, and when implementing interventions that are in themselves contested or in locations where they may be considered controversial, the evidence base must be convincing.Students on NR3 will have encountered a range of methodological approaches during Semester 1. The present module will begin by reviewing and recapping those previously covered. Thereafter, the course unit will consolidate, and compliment knowledge previously learned to set into context the toolkit of approaches available to data generation in social-ecological studies. Thus, qualitative and quantitative analyses will be introduced and reviewed for their role in evidence generation, with an emphasis on mixed-method approaches.Students taking the course unit will broadly follow the scientific process: observing and questioning, identifying research areas, generating hypotheses, experimental set ups, data analysis, reporting results. A central theory will be the reduction of uncertainty and thus, the sources of uncertainty in data collection and analysis e.g., reduction of error and redundancy in data collection and handling. Throughout the unit, students will be encouraged to consider their dissertation topics during this process by identifying which methods they may wish to employ / are feasible given the topic of interest.    The unit aims to:- Introduce students to range of methods to collect quantitative or qualitative data - Develop students quantitative and qualitative data handling skills for use in ecological and environmental management. - Increase students confidence in generating, retrieving, manipulating and presenting quantitative and qualitative data. - Enable students to understand quantitative data to facilitate the implementation of descriptive and inferential statistics.  - Introduce a range of relevant software for quantitative or qualitative data analysis  <ul><li>Describe and summarise secondary data using descriptive and basic inferential statistics.</li><li>Demonstrate data literacy including knowledge of data types, distribution, visualisation and manipulation. </li></ul> <ul><li>Appraise the suitability of data for different analyses, including interrogating sources, sampling and techniques for manipulation.</li><li>Identify some of the ethical, scientific and technological issues related to the use of quantitative data for environmental management.</li></ul> <ul><li>Retrieve and manipulate quantitative data from a variety of secondary data sources.</li><li>Analyse data for use in a range of situations and applications; including screening, cleaning and recognising parametric and non-parametric distributions.</li></ul> <ul><li>Select and use appropriate software to perform basic quantitative methods of data analysis to help understand environmental challenges.</li><li>Synthesise and present data in a variety of ways to communicate a specific environmental challenge or solution.</li></ul> The unit will be delivered through a combination of lectures, workshops and computer suite sessions where they will use Excel and be introduced to R Statistics Software and NVivo for quantitative and qualitative data analysis respectively. There will also be a single field practical based within the University campus to demonstrate data collection methods and hypothesis testing (8 hours).The in-person sessions will mostly contain workshops (10 hours) using a flipped classroom styled approach whereby students will be encouraged to participate in asynchronous 'lecture-based' pre-workshop activities (approx. 1 – 2 hours each), that will then be discussed and problem-solved in further detail during the timetabled session. This will encourage students to apply asynchronous learnings to new content during the workshops, which is particularly important where they have different levels of initial understanding. These workshops will mostly take place using computer suites. Lecture content (i.e., asynchronous, 10 hours) will cover essential guidance that provides context for empirical data analyses such as risk assessments (health and safety, ethics), and how to handle data sensitively, as well as conceptual content regarding sources of error and misinformation. Although these sessions will be asynchronous, students will nevertheless be expected to take part in interactive exercises within the sessions, with content to be reviewed pre- and post- session.   Open workbook – data exploration  1000 words 40% Weighting Professional Report - 2000 words 60% Weighting  Via the VLE within Faculty guidelines N Drinkwater, E., Robinson, E.J. and Hart, A.G., 2019. Keeping invertebrate research ethical in a landscape of shifting public opinion. Methods in Ecology and Evolution, 10(8), pp.1265-1273.Emetere, M.E., 2022. Numerical Methods in Environmental Data Analysis. Elsevier.Fox, G.A., Negrete-Yankelevich, S. and Sosa, V.J. eds., 2015. Ecological statistics: contemporary theory and application. Oxford University Press.Gardener, M., 2017. Statistics for ecologists using R and Excel: data collection, exploration, analysis and presentation. Pelagic Publishing Ltd.Shukla, S., George, J.P., Tiwari, K. and Kureethara, J.V., 2022. Data Ethics and Challenges. Springer Singapore Pte. Limited.Steel, E.A., Kennedy, M.C., Cunningham, P.G. and Stanovick, J.S., 2013. Applied statistics in ecology: common pitfalls and simple solutions. Ecosphere, 4(9), pp.1-13.Van Belle, G., 2011. Statistical rules of thumb. John Wiley & Sons.Zuur, A.F., Ieno, E.N. and Meesters, E.H., 2009. A Beginner's Guide to R (p. 150). New York: Springer.  Fieldwork 8 Lectures 10 Practical classes & workshops 26 106